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HOW SUCCESFUL WERE the “MORE” RATINGS in PREDICTING the IMPACTS of THE GLOBAL CRISIS in 2008-2009? And during 20102011? Summary Introduction ..........................................................................................................................................2 Validation of the MORE ratings on the ENTIRE bankruptcy database (Between the years: 2000-2009)..3   Validation of the MORE ratings on the ENTIRE bankruptcy database; only during the 2008&2009 crisis ..............................................................................................................................................................6   Validation of the MORE ratings on the ENTIRE bankruptcy database; second step financial crisis: 20102011......................................................................................................................................................9   Validation of the MORE rating in Continental Macro Areas ................................................................11   Validation of the MORE rating in West Europe................................................................................12   Validation of the MORE rating in East Europe.................................................................................13   Validation of the MORE rating in North America ............................................................................14   Validation of the MORE rating in Far East.......................................................................................15   Comments on Continental Macro Areas Rating Evaluation .............................................................16   Conclusions .........................................................................................................................................17   Appendix .............................................................................................................................................17  

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Introduction To validate a rating model, we have to demonstrate that: 1. For bankrupt companies, the assigned rating deteriorates, approaching the default date (Bankruptcy dynamics). 2. The model discriminates between profitable companies and bankrupt (Discriminating power of the model).

companies

For the above mentioned two steps, we need to have the information on bankrupt companies. Using the companies labeled as “bankrupt� in ORBIS (a global database which has information on 60 million companies, provided by Bureau van Dijk Electronic Publishing) we have compiled the following distribution (years: 2000-2009): Country

Number of bankrupt companies

FR

9110

BE

8172

NL

5232

IT

4529

UA

1991

RO

1207

PL

1100

FI

1098

CZ

780

LT

655

LV

598

EE

525

RU

320

SK

158

Others

318

Table 1 Number of defaulted companies in ORBIS

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Fig. 1 Distribution of defaulted companies in ORBIS

We proposed that the following methodology will be applied:

1. Validation of the MORE ratings on the ENTIRE bankruptcy database (years 20002009); 2. Validation of the MORE ratings on the ENTIRE bankruptcy database but only in 2008-2009 to enhance the knowledge of MORE ratings in the financial crisis. 3. Validation of the MORE rating in Continental Macro Areas. In each validation, we followed two steps to demonstrate the following features of the model: Bankruptcy Dynamics & Discriminating power of the model.

Validation of the MORE ratings on the ENTIRE bankruptcy database (Between the years: 2000-2009) To validate MORE ratings, first we studied the evaluation of the bankrupt companies’ ratings checking their evolution over the years. In this case, all bankrupt companies in ORBIS were used (around 40,000 companies). We observed the ratings one, two, three and four years before the FINAL available annual report.

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Fig. 2 Distribution of the ratings of defaulted companies (entire ORBIS database)

BB   B     CCC     CC     C   Fig. 3 Dynamic of defaulted companies: rating distribution and mean rating (entire ORBIS database)

In order to demonstrate the first feature, it is important to see ratings’ evolution for the bankrupt modeFinance srl Iscritta al Registro delle Imprese di Trieste PI/CF: 01168840328 E-mail: info@modefinance.com

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companies. From Fig.3, it is possible to observe that the MORE ratings catch the performance degradation of the companies with a high accuracy, approaching the default date. It was observed that one year before the last available annual report, 48% of the companies were classified as risky and 27% as vulnerable, so that 75% of companies had bad economic and financial ratings. It is very interesting to note that four years before the last accounting year, MORE ratings classified 64% of the defaulted companies as risky and vulnerable (or worse); and so MORE has a great ability to predict the default. As a second step, we had to demonstrate if the model discriminates between the non-defaulted companies and defaulted companies. To execute this, we used one of the well-known statistical methods: ROC (Relative or receiver operating characteristic, please see Appendix for details). It is possible to observe within the following graphs that the model achieved very accurate results in distinguishing defaulted companies, reaching a AUC (area under the curve ) value of 86 in the last year, with a very promising AUC value of 73 four years before the final available annual report. This behavior can be seen when the distribution of the world rating, a typical Gaussian distribution, where BB is the most probable rating class is compared to the distribution of the defaulted companies in which the most probable rating classes are the poor ones. (See Fig.4)

Fig. 4 World rating distribution: non-defaulted companies (left), defaulted companies (right). Entire ORBIS database

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Fig. 5 ROC graphs; one year before: up-left; four year before: down right. Entire ORBIS database.

In conclusion, it is possible to assert that the model predicted the world defaulted companies with very good accuracy even four years before bankruptcy occurred.

Validation of the MORE ratings on the ENTIRE bankruptcy database; only during the 2008&2009 crisis After evaluating the MORE ratings on the entire database, we wanted to extend the study to investigate if the MORE ratings recognized the financial crisis during the years 2008-2009. This is because understanding MORE ratings’ success in monitoring the crisis was quite important. To do this, the companies which went bankrupt in 2008-2009 were selected and studied in the same way as before. In this case, the database consisted of around 4,000 companies from all around the world.

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Fig .6 Distribution of the ratings of defaulted companies (2008-2009 ORBIS database)

BB   B     CCC     CC     C   Fig 7 Dynamic of defaulted companies: rating distribution and mean rating (2008-2009 ORBIS database)

In the first step; the MORE ratings once again recognized the evolution of the bankrupt companies approaching the default date. From Fig 7 it is possible to observe that one year before the final accounting year, the MORE ratings classified 75% of the defaulted companies as vulnerable (or worse). It is interesting to note that the MORE ratings had a stable behavior when companies moved towards bankruptcy, both in 2000-2009 and in the 2008-2009 crisis. This demonstrates that the MORE methodology was successful in predicting the impacts of the financial crisis.

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Again as the second step, we wanted to demonstrate if the model can discriminate between non defaulted companies and defaulted ones. As we did for the world companies between the years 20002009; we also used ROC for the world companies in the 2008- 2009 crisis. According to the AUC values, the model again achieved very accurate results in distinguishing defaulted companies reaching an AUC value of 85 in the last year with an AUC value of 73 four years before the final available annual report. This behavior is seen in the comparison between the distribution of the world rating (the distribution where BB is the most probable rating class) and the distribution of the default companies in which the most probable rating classes are the poor ones. (See Fig .8)

Fig .8 World rating distribution: non-defaulted companies (left), defaulted companies (right). 2008-2009 ORBIS database

Fig. 9 ROC graphs; one year before: up-left; four year before: down right. 2008-2009 ORBIS database

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Validation of the MORE ratings on the ENTIRE bankruptcy database; second step financial crisis: 2010-2011 For understanding in details the behavior of the MORE rating models, an ulterior analysis has been performed on the companies defaulted during the years 2010-2011. The aim is to understand if even the second step of the financial crisis, the MORE rating model is able to catch the defaulted companies. Even in this case, the bankrupted companies are been selected with the Orbis database. All around the word around 15’000 companies have been selected. The results are summarized in the following pictures where it is possible to note the evolution during the years of the defaulted companies (the method is similar to the previous testing). More than 80% of the bankrupted companies have been classified vulnerable and risky the year before the bankruptcy even (rating less than CCC). It is interesting to note than almost 70% of the companies have very low rating classes even 3 years before go to bankruptcy; this demonstrates two important points: 1) the MORE rating is capable to get the distressing of a company, even years before 2) even during the second step of the financial crisis, mainly distressed companies went to default (it is rare that “a company dies for heart attack�). The evolution of the mean value of the rating (always of defaulted companies) and the profile of the AUC/ROC curve confirm the quality of the MORE rating in default prediction.

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BB   B     CCC     CC     C  

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Validation of the MORE rating in Continental Macro Areas In order to understand better the behavior of MORE rating, we evaluated the rating performances in the different Continental Macro Area (following the definitions of ORBIS), defined in the following tables. Continental Macro Area West Europe East Europe North America Far East Rest of the World

Number of bankrupt companies 30'000 9'000 120 50 10

For statistical reasons to apply the ROC evaluation and the rating evolution, we performed the analysis on: West Europe, East Europe, North America and Far East. In all those cases we don't used the LAST available information of defaulted companies, but one year before the last year (as set by Basel II policy).

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Validation of the MORE rating in West Europe

Fig 10 Dynamic of defaulted companies: rating distribution and mean rating (West Europe ORBIS database)

BB   B     CCC     CC  

Fig 11 Mean rating evolution and ROC graph: West Europe ORBIS database

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Validation of the MORE rating in East Europe

Fig 12 Dynamic of defaulted companies: rating distribution and mean rating (East Europe ORBIS database)

BB   B     CCC     CC  

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Fig 13 Mean rating evolution and ROC graph: East Europe ORBIS database

Validation of the MORE rating in North America

Fig 14 Dynamic of defaulted companies: rating distribution and mean rating (North America ORBIS database)

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BB   B     CCC     CC  

Fig 15 Mean rating evolution and ROC graph: North America ORBIS database

Validation of the MORE rating in Far East

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Fig 16 Dynamic of defaulted companies: rating distribution and mean rating (Far East ORBIS database)

BB   B     CCC     CC  

Fig 17 Mean rating evolution and ROC graph: Far East ORBIS database

Comments on Continental Macro Areas Rating Evaluation From the graphs of the MORE rating evaluation in the different Continental Areas (Fig 10, Fig 11, Fig 12, Fig 13, Fig 14, Fig 15, Fig 16, Fig 17) it is possible to observe that the behavior of the MORE rating is quite stable and accurate. In every Continental Areas there is a clear downgrade of the ratings of the defaulted companies approaching to the bankrupt data. As it is possible to observe from the following table, the accuracy of the MORE rating is quite high all around the world. MORE rating obtains one slight difference only for the Far East evaluation where the number of bankrupt companies is very low to have an accurate statistical analysis. Continental Macro Area West Europe East Europe North America Far East

AUC value one year before bankrupt (Gini value) 0,83 (0,66) 0,83 (0,66) 0,9 (0,8) 0,77 (0,54)

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AUC value two year before bankrupt (Gini value) 0,76 (0,52) 0,77 (0,54) 0,83 (0,66) 0,64 (0,28)

AUC value three year before bankrupt (Gini value) 0,72 (0,44) 0,74 (0,48) 0,79 (0,58) 0,55 (0,1)

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Conclusions In order to validate the MORE model, we used the bankruptcy information on companies found in ORBIS. The entire database and data from the 2008-2009 crisis were taken into account. Using two well-known statistical methods, ROC & Bankruptcy Dynamic the MORE ratings achieved very ACCURATE and STABLE results for the entire database as well as data from the crisis period. The results are stable and this is essential for the validation of the rating method. From this analysis we were able to confirm the worth of the “world� (entire database) validation. In order to enhance the quality of the validation, the rating MORE has been evaluated on four different World macro regions. Also in those cases, the results are accurate and stable between different economical regions.

Appendix modeFinance vision The modeFinance vision is to look at the fundamental economic and financial aspects of the company. The main idea is to evaluate the rating by observing each aspect of the economic and financial behavior of the company: the better the equilibrium between the different aspects, the better the final rating. This idea has been implemented into the MORE (Multi Objective Rating Evaluation) rating model; the MORE model permits the user to assess the creditworthiness of a company by aggregating and evaluating the most important sections of the financial and economic behaviors of a company such as profitability, liquidity, solvency, interest coverage and efficiency.

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Unlike the most used rating models, MORE does not provide an output as a statistical measure like the probability of bankruptcy; but qualitative information about the general creditworthiness of the analyzed company. This information is expressed through a credit rating scale (shown below).

Rating class

AAA

Rating Macro class

Healthy companies

Assessment The company's capacity to meet its financial commitments is extremely strong. The company shows an excellent economic and financial flow and fund equilibrium

The company has very strong creditworthiness. It also has a good capital structure and economic and financial equilibrium. Difference from 'AAA' is slight

AA

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The company has a high solvency. The company is however more susceptible to the adverse effects of changes in circumstances and economic conditions than companies in higher rated categories

A

Capital structure and economic equilibrium are considered adequate. The company's capacity to meet its financial commitments could be affected by serious unfavourable events

BBB Balanced companies BB

The company presents vulnerable signals with regard to its fundamentals. Adverse business, financial, or economic conditions will be likely to impair the company's capacity or willingness to meet its financial commitments

B Vulnerable companies CCC

A company rated 'CCC' has a dangerous disequilibrium on the capital structure and on its economic and financial fundamentals. Adverse market events and an inadequate management could affect with high probability the company's solvency The company shows signals of high vulnerability. In the event of adverse market and economic conditions, the company's strong disequilibrium could increase

CC C

A company rated 'BB' is more vulnerable than companies rated 'BBB'. Furthermore the company faces major ongoing uncertainties or exposure to adverse business, financial, or economic conditions

Risky companies

The company shows considerable pathological situations. The company's capacity to meet its financial commitments is very low The company has not any longer the capacity to meet its financial commitments

D

The probability of default The probability of default (PD) provided by modeFinance is coupled with the rating class of each evaluated company; it is a quantitative information that expresses the possibility that a company deteriorates its financial and economical strengths; so the definition of PD is “the probability that a company goes in D class (the worst) within one year .� This means that the PD is the probability that a modeFinance srl Iscritta al Registro delle Imprese di Trieste PI/CF: 01168840328 E-mail: info@modefinance.com

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company will face distress. In order to evaluate the probability of default, modeFinance observes the historical variations of the ratings distribution during the years by using the transition matrix theory (as shown in the figure below). This means computing the frequency of the rating downgrades in D class by observing a long historical series of ratings.

This definition of PD comprehends even the probability of bankruptcy; because according to modeFinance definition, a company rated D should be restructured financially for not going bankrupt. In order to validate its rating model and the PD definition, modeFinance tested it on the capability to discriminate the profitable companies from the non-profitable companies (following the Basel II rules of Bank for International Settlements and the most known validation techniques). In the following section is presented an example regarding the validation of the ratings evaluated for French companies.

ROC The MORE ratings have been tested by using ROC (relative or receiver operating characteristic) method and computing the AUC (area under the curve). ROC curves generalize contingency table analysis by providing information on the performance of a model at any cut-off that might be chosen. ROCs are constructed by scoring all credits and ordering the non-defaulters from worst to best on the x axis and then plotting the percentage of defaults excluded at each level on the y axis. So, the y axis is formed by associating every score on the x axis with the cumulative percentage of defaults with a score equal to or worse than that score in the test data. In other words, the y axis gives the percentage of defaults excluded as a function of the number of non-defaults excluded. A convenient measure for summarizing the graph of the ROC is the area under the ROC (AUC), which is calculated as the proportion of the area below the ROC relative to the total area of the unit square. A value of 0.5 indicates a random model, and a value of 1.0 indicates perfect discrimination. A rough guide for classifying the accuracy of a diagnostic test is the traditional academic point system:

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• • • • •

1,00-0,90: excellent 0,90-0,80: good 0,80-0,70: adequate 0,70-0,60: poor 0,60-0,50: fail

Fig 18 Example of ROC analysis

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MORE Rating Validation  

MORE Rating Validation